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Unmanned aerial vehicles towards future Industrial Internet:Roles and opportunities

Digital Communications and Networks 2024年第4期10卷 873-883页

作者： Linpei Li Chunlei Sun Jiahao Huo Yu Su Lei Sun Yao Huang Ning Wang Haijun Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China China Mobile Chengdu Institute of Research and Development Chengdu 610200China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Chengdu University of Information Technology Chengdu 610225China Zhengzhou University Zhengzhou 450001China

Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site *** UAVs to assist communications is one of the promising applications and research *** future Industrial Internet places higher demands on communication *** easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial ***,UAVs are considered as an integral part of Industry *** this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first ***,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are *** to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain ***,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.

关键词： Unmanned aerial vehicles(UAVs) UAV-assisted communications Industrial Internet

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Random Separating Hyperplane Theorem and Learning Polytopes 51

Random Separating Hyperplane Theorem and Learning Polytopes

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Bhattacharyya, Chiranjib Kannan, Ravindran Kumar, Amit Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Operations Research Carnegie Mellon University Pittsburgh United States Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773225

The Separating Hyperplane theorem is a fundamental result in Convex Geometry with myriad applications. The theorem asserts that for a point a not in a closed convex set K, there is a hyperplane with K on one side and a strictly on the other side. Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes. RSH asserts that if the distance between a and a polytope K with k vertices and unit diameter in ℜd is at least δ, where δ is a fixed constant in (0,1), then a randomly chosen hyperplane separates a and K with probability at least 1/poly(k) and margin at least Ω(δ/√d). RSH has algorithmic applications in learning polytopes. We consider a fundamental problem, denoted the "Hausdorff problem", of learning a unit diameter polytope K within Hausdorff distance δ, given an optimization oracle for K. Using RSH, we show that with polynomially many random queries to the optimization oracle, K can be approximated within error O(δ). To our knowledge, this is the first provable algorithm for the Hausdorff Problem in this setting. Building on this result, we show that if the vertices of K are well-separated, then an optimization oracle can be used to generate a list of points, each within distance O(δ) of K, with the property that the list contains a point close to each vertex of K. Further, we show how to prune this list to generate a (unique) approximation to each vertex of the polytope. We prove that in many latent variable settings, e.g., topic modeling, LDA, optimization oracles do exist provided we project to a suitable SVD subspace. Thus, our work yields the first efficient algorithm for finding approximations to the vertices of the latent polytope under the well-separatedness assumption. This assumption states that each vertex of K is far from the convex hull of the remaining vertices of K, and is much weaker than other assumptions behind algorithms in the literature which find vertices of the latent polytope. © Chiranjib Bhatt

关键词： Set theory

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Clustering Model-based Analysis of Sales in Vegetable Categories for Supermarkets 2

Clustering Model-based Analysis of Sales in Vegetable Catego...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liao, Jiana Chen, Jiajun Zheng, Wenxu Ren, Yazhou Guangdong University of Technology School of Automation Guangzhou China University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798331515669

The development of effective vegetable sales strategies is crucial for enhancing supermarket profitability, and these strategies are heavily reliant on a nuanced understanding of the sales volume distribution patterns across different vegetable categories. However, most existing studies fail to thoroughly analyze these distribution patterns, often neglecting factors such as sales volume magnitude and fluctuations. To address this gap, we first propose a comprehensive framework based on a K-means++ clustering model and Spearman correlation coefficient to analyze the sales data. Firstly, we normalize the data to eliminate the impact of differing dimensions across vegetable categories. Next, we analyze the sales data from both quarterly and daily perspectives. Data visualization is performed utilizing line charts, followed by a descriptive analysis of the sales volume distribution considering both magnitude and fluctuation. Furthermore, we utilize a K-means++ clustering model and the Spearman correlation coefficient to further explore the distribution patterns and interrelationships among different vegetable categories. The results of the experiments reveal the daily and quarterly sales distribution patterns of various vegetable categories, providing valuable insights to support the development of effective vegetable sales strategies for supermarkets. © 2024 IEEE.

关键词： Sales

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TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering 39

TPCH: Tensor-interacted Projection and Cooperative Hashing f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Zhongwen Li, Xingfeng Sun, Yinghui Sun, Quansen Sun, Yuan Ling, Han Dai, Jian Ren, Zhenwen School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Engineering Southeast University China College of Computer Science Sichuan University China Southwest Automation Research Institute China South Industries Group Corporation China School of National Defence Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)157735897X

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sorting

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Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models 41

Enhancing Storage and Computational Efficiency in Federated ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Zixin Qi, Fan Xu, Changsheng School of Computer Science and Engineering Tianjin University of Technology Tianjin China Institute of Automation Chinese Academy of Sciences Beijing China

The remarkable generalization of large-scale models has recently gained significant attention in multimodal ***, deploying heterogeneous large-scale models with different modalities under Federated Learning (FL) to protect data privacy imposes tremendous challenges on clients' limited computation and *** this work, we propose M2FEDSA to address the above *** realize modularized decomposition of large-scale models via Split Learning (SL) and only retain privacy-sensitive modules on clients, alleviating storage *** freezing large-scale models and introducing two specialized lightweight adapters, the models can better focus on task-specific knowledge and enhance modality-specific knowledge, improving the model's adaptability to different tasks while balancing *** addition, M2FEDSA further improves performance by transferring multimodal knowledge to unimodal clients at both the feature and decision levels, which leverages the complementarity of different *** experiments on various multimodal classification tasks validate the effectiveness of our proposed *** code is made available publicly at https://***/M2FedSA/M-2FedSA. Copyright 2024 by the author(s)

关键词： Federated learning

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From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization 30

From News to Summaries: Building a Hungarian Corpus for Extr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit HUN-REN Institute for Computer Science and Control Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

ISBN: (纸本)9782493814104

Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： abstractive summarization extractive summarization Hungarian

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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PANSA: A Framework for Threat Knowledge Graph Construction 3

PANSA: A Framework for Threat Knowledge Graph Construction

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Chen, TianXiang Zhuang, Yi Xu, Tao Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9798331506582

Cyber intelligence provides new prevention ideas for cyberattacks, but it is difficult to use effectively due to its multi-source heterogeneity and fragmentation. To address this problem, we propose a threat knowledge graph construction framework based on Pretrained Adversarial NER and relationship Subgraph-based Alignment called PANSA. First, the cyber threat domain ontology model is constructed in combination with the existing cyber threat intelligence standards. Secondly, SecBABC threat entity recognition model is designed, which incorporates pre-training and adversarial training modules to extract high-quality threat intelligence data. Then the Multi-Relation GAT for Entity Alignment framework (MRGEA) is designed. MRGEA considers the impact of different relationships on entities, and uses a multi-relation graph attention network to mine deep information. Finally, the cyber threat knowledge graph is visualized. Experimental results show that PANSA effectively integrates security intelligence and outperforms existing methods in knowledge extraction and entity alignment. © 2024 IEEE.

关键词： Knowledge graph

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Switching Classes: Characterization and Computation 49

Switching Classes: Characterization and Computation

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Antony, Dhanyamol Cao, Yixin Pal, Sagartanu Sandeep, R.B. Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science & Engineering Indian Institute of Technology Dharwad India

ISBN: (纸本)9783959773355

In a graph, the switching operation reverses adjacencies between a subset of vertices and the others. For a hereditary graph class G, we are concerned with the maximum subclass and the minimum superclass of G that are closed under switching. We characterize the maximum subclass for many important classes G, and prove that it is finite when G is minor-closed and omits at least one graph. For several graph classes, we develop polynomial-time algorithms to recognize the minimum superclass. We also show that the recognition of the superclass is NP-hard for H-free graphs when H is a sufficiently long path or cycle, and it cannot be solved in subexponential time assuming the Exponential Time Hypothesis. © Dhanyamol Antony, Yixin Cao, Sagartanu Pal, and R. B. Sandeep.

关键词： Graph algorithms

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